Atadian Analytics

Our thinking around analytics business in Web3
With so much data being generated every single day (both on and off-chain) and endless possibilities of what can be built, how does one go about building an analytics business for Web3?
The explosive growth of Web3 promises the Renaissance of inclusive ownership economy and the idea of decentralized governance.
It promises a world where if you create value, value finds you and rewards you fairly and efficiently. A world where critical decisions about who gets what and why aren’t made behind closed doors by the select few. A world where essential services can be accessible to all, no matter where or who you are.
We believe in this vision and would like to see it come to fruition — both in our own company and in the world.
But like any innovation, the functionings of Web3 have their own challenges — a fair bit of space we can jump in to help, if you will.
The challenges we are excited to tackle are issues surrounding trust and credit worthiness, on-chain retail analytics, and asset evaluation for the metaverse. These are all difficult to crack but also the rewards and impact are phenomenal.
Input-Output Schema
In a nutshell, we take on-chain, off-chain, and multi-chain data as input to productize them into services that generate value to the Web3 ecosystems. Our tokens will be valued based on these utilities.
Along this journey, we foresee many decisions that should be decided together at the DAO level, not by algorithms or our team alone.
Our stances:
  1. 1.
    360-degree data platform: on-chain, off-chain, and eventually multi-chain
  2. 2.
    build well-defined data products that help enable use-cases that are larger than ourselves
  3. 3.
    reward data owners every time Atadia makes money off of their data (it's the Web3 spirit)
  4. 4.
    everything we do should come back to enhance the value of our tokens
Potential revenue models:
  1. 1.
    Data brokerage - Per request transaction fees
  2. 2.
    Direct product fees - depending on the types of products we run with
For example, in the PFPscore case:
  • Data brokerage fee model ==> PFPscore checks
  • Direct product fees ==> Mint-Now-Pay-Later or Uncollateralized Lending fee